To deal with the challenging problem of recognizing the small number of distinguishable genes which can tell the cancer patients from normal people in a dataset with a small number of samples and tens of thousands of genes, novel hybrid gene selection algorithms are proposed in this paper based on the statistical correlation and K-means algorithm. The Pearson correlation coefficient and Wilcoxon signed-rank test are respectively adopted to calculate the importance of each gene to the classification to filter the least important genes and preserve about 10 percent of the important genes as the pre-selected gene subset. Then the related genes in the pre-selected gene subset are clustered via K-means algorithm, and the weight of each gene is calculated from the related coefficient of the SVM classifier. The most important gene, with the biggest weight or with the highest votes when the roulette wheel strategy is used, is chosen as the representative gene of each cluster to construct the distinguishable gene subset. In order to verify the effectiveness of the proposed hybrid gene subset selection algorithms, the random selection strategy (named Random) is also adopted to select the representative genes from clusters. The proposed distinguishable gene subset selection algorithms are compared with Random and the very popular gene selection algorithm SVM-RFE by Guyon and the pre-studied gene selection algorithm SVM-SFS. The average experimental results of 200 runs of the aforementioned gene selection algorithms on some classic and very popular gene expression datasets with extensive experiments demonstrate that the proposed distinguishable gene subset selection algorithms can find the optimal gene subset, and the classifier based on the selected gene subset achieves very high classification accuracy.